Introduction to machine learning
ML is a discipline that heavily correlates with the discipline of statistics. We will go through the basics of ML at a high level so that we can appreciate the tooling mentioned later in this chapter.
Understanding data
ML is the process of using some type of learning algorithm on a set of historical data to predict things that are unknown, such as image recognition and future event forecasting, to name a few. When you’re feeding data into your ML model, you will use features. A feature is just another term for data. Data is the oil that runs ML, so we will talk about that first.
Types of data
- Quantitative data: Quantitative data is data that can be boxed in and measured. Data such as age and height are good examples of quantitative data. Quantitative data can come in two flavors: discrete and continuous. Discrete data is data that is countable and finite or has a limited range of values. An example...
